Social distance monitoring is a complex task that relies essentially on mapping image coordinates to real-world coordinates. In this context, several works investigated mapping functions. In [1]}, authors used the inverse homography transformation matrix, while [2]} relied on the convex lens geometric properties and the dimensions of bounding boxes to estimate the depth of each detected individual. Moreover, authors of [3]} focused on implementing a camera calibration method to estimate intrinsic and extrinsic camera parameters. They developed a corner detection algorithm to establish the real-world coordinates system.
In [4]}, individuals' real coordinates were estimated using the characteristics of the used UAV-mounted camera and a calibration constant defined based on the area expansion principle, while homography based estimation was realized in [5]} by relaying on capturing at least four landmarks.
